Practice Before Theory: Why Innovation Starts with Doing

Daniel Lemire challenges the idea that theoretical understanding must come before innovation, arguing that progress often results from observing what works in practice. He critiques 'thinkism,' the belief that pure thought can solve complex problems, which is often reinforced by traditional schooling but fails in real-world R&D. Ultimately, he suggests that breakthroughs require more experimentation and less reliance on abstract theory or existing scholarship.

Key Points

• The linear theory of innovation, which posits that theory always precedes practice, is often historically and practically incorrect.
• 'Thinkism' is a flawed approach that prioritizes abstract reasoning over hands-on experience and observation.
• In research and development, discoveries usually stem from observing something that works and then formalizing it, rather than starting with a complete understanding.
• The world's complexity means that no individual or AI can solve all problems through scholarship alone; practical experimentation is essential.

Sentiment

The community is moderately sympathetic to the core thesis that practice often precedes theoretical understanding, but there is significant pushback against what many see as an overly anti-academic framing. The prevailing view is that the article creates a false dichotomy: both thinking and doing are essential, and the real skill lies in knowing when to apply each. The education sub-debate is sharply divided but largely tangential to the article's main point.

In Agreement

• Hands-on experience reveals real-world constraints that purely theoretical approaches miss, and academic literature often ignores these constraints to its detriment
• The concept of 'thinkism' usefully captures a failure mode in large organizations where committees over-plan instead of experimenting
• Historical examples confirm that practical discoveries frequently precede formal theoretical understanding
• Education should incorporate more 'doism' — learning by building and experimenting rather than memorizing and reproducing
• AI's limitations support the article's point: even vast knowledge is insufficient without experimentation, though AI does accelerate the trial-and-error cycle

Opposed

• The article presents a false dichotomy — both theory and practice are essential tools that should be used in conjunction, not pitted against each other
• Self-taught developers underestimate how much they benefit from accumulated theoretical knowledge and 'standing on the shoulders of giants'
• Academia and industry have fundamentally different goals; judging academic research by its immediate practical applicability misses the point of pure learning
• The article's exam analogy is flawed because real-world work allows collaboration, research time, and iteration that timed exams don't
• Theoretical design is not 'worthless' — try building anything complex in the physical world without a design and you'll waste enormous time and materials